Construction and Validation of a Prediction Model for the Efficacy of Triple-negative Breast Cancer Based on the Glycolysis-related Gene GFUS
Objective To explore the predictive value of glycolysis-related gene GDP-L-amylose synthase(GFUS)on the prognostic profile of triple-negative breast cancer(TNBC)based on bioinformatics database information.Methods The gene expression information of TNBC was obtained from the Cancer Genome Atlas(TCGA)database.The differential expression and prognostic differences of GFUS in TNBC were analyzed by bioinformatics methods.The co-expressed genes with GFUS were screened to establish a prognostic model for TNBC and verified.Results The gene transcriptome data of 160 TNBC tissues and 113 normal tissues were downloaded from the TCGA database,which verified that the high expression of GFUS in TNBC was associated with poor prognosis.Based on GFUS co-expressed genes,three genes related to TNBC prognosis were obtained by COX regression analysis to construct the model.According to the prognostic model,the external data set training samples were divided into high-risk group(45 cases)and low-risk group(62 cases).Kaplan-Meier survival analysis showed that the total survival of the high-risk group was lower than that of the low-risk group,and the difference was statistically significant(P＜0.05).Conclusion The model based on the glycolysis-related gene GFUS can effectively predict the prognosis of TNBC and provide new ideas for individualized treatment.
Triple-negative breast cancerGDP-L-amylose synthasePrognostic model
国家科技期刊平台
NSTL
万方数据
维普
